Abstract
The accurate identification of offshore wind power ramp events has great effects on wind power forecast. In order to improve the prediction accuracy of offshore wind power, this paper proposes an XGBoost-GRU combined forecasting model considering the number of climbing features. Firstly, the adaptive revolving door algorithm is used to identify the wind power climbing event, as well as data compression and feature extraction. Then, the XGBoost decision tree and gating loop unit are used to make preliminary power prediction. In case studies, the results are weighted and combined in detail. It is proved that the proposed model has a terrific performance on the offshore wind power prediction.
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Acknowledgment
This work was supported by Science and Technology Project of China Huaneng Group Co., Ltd. (NO. HNKJ20-H66, Offshore Wind Power Site Selection and Associated Technologies for the Deep-sea areas).
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Yin, L. et al. (2024). A New Combination Model for Offshore Wind Power Prediction Considering the Number of Climbing Features. In: Li, J., Zhang, B., Ying, Y. (eds) 6GN for Future Wireless Networks. 6GN 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 553. Springer, Cham. https://doi.org/10.1007/978-3-031-53401-0_31
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DOI: https://doi.org/10.1007/978-3-031-53401-0_31
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